AWS SageMaker provides several built-in image classification algorithms including:

1. Linear Learner Algorithm: This algorithm is used for binary and multiclass classification. It scales across multiple GPUs and machines.
2. XGBoost Algorithm: This is an implementation of the gradient boosted trees algorithm and is used for binary and multiclass classification.
3. Image Classification Algorithm: This is a supervised learning algorithm that supports multilabel classification. It takes an image as input and outputs one or more labels assigned to that image.
4. Object Detection Algorithm: This algorithm detects and classifies objects in images.
5. Semantic Segmentation Algorithm: This algorithm assigns a class label to each pixel in the image.
6. K-Nearest Neighbors (k-NN) Algorithm: This is a non-parametric method used for classification and regression.
7. Neural Topic Model (NTM) Algorithm: This is an unsupervised learning algorithm that is used to organize a corpus of documents into topics that contain word groupings based on their statistical patterns.